AIMC Topic: Biomarkers

Clear Filters Showing 661 to 670 of 2219 articles

Dual biomarkers CT-based deep learning model incorporating intrathoracic fat for discriminating benign and malignant pulmonary nodules in multi-center cohorts.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND: Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate ben...

Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.

Scientific reports
Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathophysiologically unrelated, AD is a common skin disease characterized by chronic inflammation and skin barrier dysfunction, whereas T2DM is a metabolic ...

Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis.

Mediators of inflammation
This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis ...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

Clinical biochemistry
BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracellular matrix (ECM) components offer significant diagnostic potential. This study evaluates ECM-relat...

Mapping Thrombosis Serum Markers by H-NMR Allied with Machine Learning Tools.

Molecules (Basel, Switzerland)
Machine learning and artificial intelligence tools were used to investigate the discriminatory potential of blood serum metabolites for thromboembolism and antiphospholipid syndrome (APS). H-NMR-based metabonomics data of the serum samples of patient...

Identification and verification of the optimal feature genes of ferroptosis in thyroid-associated orbitopathy.

Frontiers in immunology
BACKGROUND: Thyroid-associated orbitopathy (TAO) is an autoimmune inflammatory disorder of the orbital adipose tissue, primarily causing oxidative stress injury and tissue remodeling in the orbital connective tissue. Ferroptosis is a form of programm...

IL-27 as a novel biomarker for pruritus in nodular prurigo and bullous pemphigoid.

Frontiers in immunology
INTRODUCTION: Bullous pemphigoid (BP) and prurigo nodularis (PN) are chronic pruritic skin diseases that severely impact patients' quality of life. Despite the widespread attention these two diseases have garnered within the dermatological field, the...

A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.

PeerJ
BACKGROUND: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and c...

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.

PloS one
With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by prov...